1. Field of the Invention
The present invention relates generally to the field of chemical reactors, and, more particularly, the present invention relates to a chemical reactor which is optimally controlled through the use of neural networks.
2. Description of the Related Art
Discontinuous changes of a reactor can suddenly and unknowingly occur in continuously operated chemical reactors. For example, when the temperature rises greatly or if the reaction yield drops rapidly with an unsuspected discontinuous change of the reactor state, this leads to production outages and reactor malfunctions. These phenomena can be explained as follows by what are referred to as critical states.
The dynamic behavior of a continuous reactor is mathematically described by a parameter-dependent differential equation, EQU x=f(x,.mu.) (1).
The variable x is a vector. It contains all reactor states. For example, these can be the concentrations of the participating chemical compounds or the temperature of the mixture etc.
The behavior of the reactor is determined by a plurality of external influencing quantities .mu. that are determined by the operator or the environment. For example the average dwell time of the substances in the reactor kettle, the cooling rate, the reaction rates of the substances in the reactor kettle or the kettle pressure of the reactor are all types of influencing qualities. The reactor is operated in a stationary, i.e. chronologically constant state, i.e. EQU x=0=f(x,.mu.) (2).
Depending on the existing parameter value .mu., there are usually a plurality of possible stationary states x(.mu.) that are connected to one another in an extremely complicated way. A critical state occurs where, given variation of the parameters .mu., a stationary state x(.mu.) ceases to exist or becomes unstable. The reactor then changes discontinuously from one stationary state to the next or begins to oscillate. This can lead to uncontrollable situations.
There are no sufficiently precise mathematical models available for the dynamic description of the processes sequencing in the reactor. All possible external influencing quantities are also not known. Important parameters .mu. can consequently vary without being noticed at first. This leads to malfunctions if critical points are reached.
It is known that the dynamic behavior of a continuous reactor in a local environment of a critical state can be mathematically described by a parameter-dependent differential equation, the so-called normal form (J.Guckenheimer et al., Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields, Springer-Verlag, ISBN 0-540-90819-6, pp.117-165, 1983). Each normal form represents exactly one type of a critical state. The present invention solves the problem of determining state parameters of a chemical reactor through the use of artificial neural networks.